Julia, Matlab, etc. index arrays starting at 1. C, python, etc. index starting at 0. In a dense array, we can simply subtract one from the index, and in fact, this is what Julia will does under the hood when you pass a vector between C to Julia.
However, for sparse array formats, it's not just a matter of subtracting one
from the index, as the internal lists of indices, positions, etc all start from
zero as well. To remedy the situation, this package exports a handy zero-indexed integer
type called CIndex. The internal representation of CIndex is one less than the
value it represents, and we can use CIndex as the index or position type of
a sparse array to represent arrays in other languages.
For example, if idx_c, ptr_c, and val are the internal arrays of a CSC
matrix in a zero-indexed language, we can represent that matrix as a one-indexed
Cindex array without copying by calling
julia> m = 4; n = 3; ptr_c = [0, 3, 3, 5]; idx_c = [1, 2, 3, 0, 2]; val = [1.1, 2.2, 3.3, 4.4, 5.5];
julia> ptr_jl = unsafe_wrap(Array, reinterpret(Ptr{CIndex{Int}}, pointer(ptr_c)), length(ptr_c); own = false)
4-element Vector{CIndex{Int64}}:
CIndex{Int64}(0)
CIndex{Int64}(3)
CIndex{Int64}(3)
CIndex{Int64}(5)
julia> idx_jl = unsafe_wrap(Array, reinterpret(Ptr{CIndex{Int}}, pointer(idx_c)), length(idx_c); own = false)
5-element Vector{CIndex{Int64}}:
CIndex{Int64}(1)
CIndex{Int64}(2)
CIndex{Int64}(3)
CIndex{Int64}(0)
CIndex{Int64}(2)
julia> A = SparseMatrixCSC{Float64, CIndex{Int}}(m, n, ptr_jl, idx_jl, val)
4×3 SparseMatrixCSC{Float64, CIndex{Int64}} with 5 stored entries:
⋅ ⋅ 4.4
1.1 ⋅ ⋅
2.2 ⋅ 5.5
3.3 ⋅ ⋅